Set Of Criteria Research Articles

Selecting scaling indicators in structural equation models (sems).

It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, the choice of which indicator to use for this purpose receives little attention, and many analysts use the first indicator without considering whether there are better choices. When all indicators of the latent variable have essentially the same properties, then the choice matters less. But when this is not true, we could benefit from scaling indicator guidelines. Our article first demonstrates why latent variables need a scale. We then propose a set of criteria and accompanying diagnostic tools that can assist researchers in making informed decisions about scaling indicators. The criteria for a good scaling indicator include high face validity, high correlation with the latent variable, factor complexity of one, no correlated errors, no direct effects with other indicators, a minimal number of significant overidentification equation tests and modification indices, and invariance across groups and time. We demonstrate these criteria and diagnostics using two empirical examples and provide guidance on navigating conflicting results among criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Professionals’ views on the concepts of translation: the challenge of categorisation

ABSTRACT In this article, we analyse and discuss translation professionals’ views on the concepts of their trade: the concept of translation itself but also adjacent notions such as subtitling, interpreting, localisation and transcreation. Through focus groups with translators and translation project managers working for a translation agency, we explore how practitioners understand the concepts of translation, how they categorise and why they categorise as they do. Data were analysed by means of prototype theory, and results show that the focus-group participants understand prototypical translation rather narrowly as source-text oriented, written, interlingual transfer, but also that they have a broad and nuanced understanding of translation as a category. Beyond the prototype, they use ‘translation’ as a cover term for a wide variety of tasks that involve different degrees of intervention, transformation and creativity. Interestingly, the analyses show that the practitioners in the study categorise quite differently than translation scholars do. They draw on a fluctuating set of criteria for categorisation, some of which are highly pragmatic (e.g. pricing), and their definitions are much more floating and context-sensitive. In fact, they operate with different concepts depending on whom they interact with: clients, colleagues – or scholars.

Comparison of Homoclinic Bifurcations Between Grid-Following and Grid-Forming Converters

A second-order model is developed to describe the essential behavior of the grid-following converter (GFLC) and the grid-forming converter (GFMC) for studying the synchronization characteristics of grid-connected converter systems. In this paper, a general set of criteria in terms of the manifolds and saddle quantity is derived for studying the homoclinic bifurcation behavior of GFLCs and GFMCs. It is shown that three unstable periodic orbits and two homoclinic bifurcations may exist for the GFLC, while only one stable periodic orbit and one homoclinic bifurcation may exist for the GFMC. It is also found that after the onset of the first homoclinic bifurcation, the stable equilibrium point (SEP) is surrounded by the stable manifolds of the unstable equilibrium point (UEP), which forms the SEP's basin of attraction, for both GFLCs and GFMCs. In this case, the converters may lose their synchronization if the trajectory crosses over the UEP. Therefore, system parameters should be designed to avoid the onset of the aforementioned bifurcation so that the SEP's basin of attraction can be significantly enlarged to contain the UEP. As a result, the GFLC or GFMC can eventually resynchronize with the grid even when its trajectory passes the UEP. Finally, experimental results are provided to verify these theoretical findings.

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.

Social innovation initiatives of NGOs in a Hungarian disadvantaged area

The study examines social innovation endeavours initiated by non-profit organisations in the Abaúji region53. Addressing the challenges facing the region (emigration, ageing, unemployment and educational inequalities) requires the accurate identification of local needs and the involvement of the population in decision-making. One of the tools for these initiatives is social innovation and support for social innovation endeavours. The key actors in the social innovation process are non-profit organisations (NGOs), which see social innovation primarily as a mission. The aim of our study is to present social innovation solutions initiated by NGOs in the region and to explore the possibility of their adaptation as good practice. The study alsoidentifies the main sets of criteria that, in addition to providing a structured record of each case study, will help to make good practices comparable. Documentation against the identified criteria will support the process of adaptation, which is critical to the successful implementation of social innovation initiatives.

Responsible research assessment in the area of quantitative methods research: A comment on Gärtner et al.

In this commentary, we discuss the proposed criteria in Gärtner et al. (2022) for hiring or promoting quantitative methods researchers. We argue that the criteria do not reflect aspects that are relevant to quantitative methods researchers and typical publications they produce. We introduce a new set of criteria that can be used to evaluate the performance of quantitative methods researchers in a more valid fashion. We discuss the necessity to balance scientific expertise and open science commitment in such ranking schemes.

Prioritizing Causation in Decision Trees: A Framework for Interpretable Modeling

As a popular machine learning model, decision trees classify and generalize well, but face challenges in engineering applications: 1) Sensitivity to perturbations and lack of interpretability due to correlation reliance. 2) Manual setting of stopping criterion which is unrelated to correlation strength and easily leads to over-partitioning. To address these two challenges, we first theoretically analyze what leads to sub-optimal decision trees. By incorporating causal discovery, this limitation can be attributed to the fact that trees grown with spurious correlations often fall into sub-optimal that lead to overfitting and unfair behaviors. Neglecting causality motivates us to develop a ‘better’ tree with low Kolmogorov complexity and high generalization capability. Then we propose a causality decision tree framework, CausalDT, based on our theoretical expectation, where Hilbert-Schmidt independence criterion serves as a baseline. Unlike previous approaches that prioritize relevance, our framework determines branch nodes based on causation between features, with the significance level determining whether the tree should be expanded further. Experimental results demonstrate that our model maintains performance while reducing average tree depth by 35% on various datasets. Furthermore, our model enhances decision fairness and interpretability.

Assessment of the association of the rs12329760 polymorphism of the <i>TMPRSS2</i> gene with acute coronary syndrome in patients with new coronavirus infection

Introduction. Cardiovascular diseases (СVD) rank first in terms of morbidity and mortality not only in the Russian Federation but throughout the world. Since the end of 2019 a pandemic of Severe Acute Respiratory Syndrome (SARS) or new coronavirus infection (NCI) has spread throughout the world, which contributed to the development and course of CVD. Therefore, genetic markers began to be considered as factors potentially influencing CVD, its development and severity. Objective: To evaluate the association of the nucleotide sequence variant rs12329760 of the TMPRSS2 gene with acute coronary syndrome in patients who have had a new coronavirus infection.Material and methods. The study included 100 patients (women – 50, men – 50) with ACS and previous NCI, who were selected on the basis of a positive PCR test for the presence of SARS-CoV-2 in the anamnesis, hospitalized at the regional vascular center No. 7 of the City Clinical Hospital No. 2 of the city of Novosibirsk. Women age was 59.5 ± 7.2 years, men age was 53.5 ± 9.3 years. The diagnosis of ACS was established according to a set of criteria proposed and established by the Russian Society of Cardiology and in accordance with the updated clinical recommendations of the Ministry of Health of the Russian Federation of 2020. Patients underwent clinical and instrumental examination, coronary angiography with possible stenting, as provided for in the standards of medical care and clinical guidelines. The nucleotide sequence variant rs12329760 of the TMPSS2 gene was determined in patients using PCR with further analysis of restriction fragment length polymorphism. The comparison group consisted of 200 patients with ACS without previous NCI (no positive PCR test, no positive antibodies).Results. Carriage of CC, СТ, TT, genotype variants of the nucleotide sequence rs12329760 of the TMPSS2 gene are not associated with an increased risk of developing ACS in patients with previous NCI. When comparing the frequencies of the rs12329760 genotypes of the TMPSS2 gene in groups with ACS with NCI and ACS without NCI, no statistically significant differences were obtained. In the group with ACS with NCI, carriage of the homozygous genotype was more common (p = 0.011).Conclusions. Variants of the nucleotide sequence rs12329760 of the TMPSS2 gene are not associated with ACS with previous NCI.

Finite mixture models for multiscale dynamics in Chinese financial markets

A novel statistical machine learning technique for decomposing, analyzing, and modeling complex time series is presented in this paper. This methodology is based on unsupervised classification methods using finite mixtures, which have been widely used in factor analysis to extract essential information from multivariate static data in a parsimonious manner. Here, they are applied in a purely dynamic framework to create a new family of autoregressive models. The selection of the number of components and appropriate geometric template in these models is based on a set of information criteria that require solving a multiple objective optimization problem. To ensure the most adequate models are selected, the problem is augmented with a simulation instruction, providing a robust criterion for assignment. Experiments on real financial data sampled over the COVID-19 pandemic demonstrate the potential of this approach as a promising alternative to the well-known Box-Jenkins methodology. The proposed method offers flexibility, accuracy, and stability, making it suitable for a variety of applications in financial modeling and other fields where complex time series data analysis is required.

Factors predicting mortality in hospitalised HIV-negative children with lower-chest-wall indrawing pneumonia and implications for management.

In 2012, the World Health Organization revised treatment guidelines for childhood pneumonia with lower chest wall indrawing (LCWI) but no 'danger signs', to recommend home-based treatment. We analysed data from children hospitalized with LCWI pneumonia in the Pneumonia Etiology Research for Child Health (PERCH) study to identify sub-groups with high odds of mortality, who might continue to benefit from hospital management but may not be admitted by staff implementing the 2012 guidelines. We compare the proportion of deaths identified using the criteria in the 2012 guidelines, and the proportion of deaths identified using an alternative set of criteria from our model. PERCH enrolled a cohort of 2189 HIV-negative children aged 2-59 months who were admitted to hospital with LCWI pneumonia (without obvious cyanosis, inability to feed, vomiting, convulsions, lethargy or head nodding) between 2011-2014 in Kenya, Zambia, South Africa, Mali, The Gambia, Bangladesh, and Thailand. We analysed risk factors for mortality among these cases using predictive logistic regression. Malnutrition was defined as mid-upper-arm circumference <125mm or weight-for-age z-score <-2. Among 2189 cases, 76 (3·6%) died. Mortality was associated with oxygen saturation <92% (aOR 3·33, 1·99-5·99), HIV negative but exposed status (4·59, 1·81-11·7), moderate or severe malnutrition (6·85, 3·22-14·6) and younger age (infants compared to children 12-59 months old, OR 2·03, 95%CI 1·05-3·93). At least one of three risk factors: hypoxaemia, HIV exposure, or malnutrition identified 807 children in this population, 40% of LCWI pneumonia cases and identified 86% of the children who died in hospital (65/76). Risk factors identified using the 2012 WHO treatment guidelines identified 66% of the children who died in hospital (n = 50/76). Although it focuses on treatment failure in hospital, this study supports the proposal for better risk stratification of children with LCWI pneumonia. Those who have hypoxaemia, any malnutrition or those who were born to HIV positive mothers, experience poorer outcomes than other children with LCWI pneumonia. Consistent identification of these risk factors should be prioritised and children with at least one of these risk factors should not be managed in the community.

Digital Games Adopted by Adults—A Documental Approach through Meta-Analysis

The gaming industry has seen a considerable expansion thanks to the ever-increasing and widespread consumption of digital games in different contexts of use and across all age groups. We are witnessing a commercial boom and awakening the attention of researchers from different scientific areas to address an interdisciplinary topic. Digital games consumption has inspired some studies investigating the use and adoption of these games and, in this context, we ask: “how has the use and adoption of digital games by adults been studied?”. We conducted a documental study with a meta-analysis approach to answer these questions, considering the most relevant research papers published in the last fifteen years, according to a set of inclusion criteria. The planned objectives consider identifying the main dimensions in the studies about the use and adoption of digital games by adults and the findings of this study delineate several dimensions as prospective latent variables for inclusion in future studies within acceptance models for digital games. Furthermore, our research illuminates the socialization dimension, particularly when amalgamated with other conceptual dimensions. This nuanced understanding underscores the intricate interplay between various factors influencing the acceptance and adoption of digital gaming technologies.

Business Intelligence Data-driven Assistant (BIDDA) for MSMEs using Setup Predictive Performance Model

The Department of Science and Technology (DOST) is offering soft loan service under the Small Enterprise Technology Upgrading Program (SETUP), to assist Micro Small and Medium Enterprises (MSME) to avail useful technologies and machineries in order to improve their operations. Low rate of repayments affects the agency’s operations and the government. This paper aimed to create a predictive system analysis to foresee the success of loan repayment in the agency, particularly in SETUP Program. MSMEs may enroll in this program through project proposal, indicating the marketing aspect; technological aspect; and financial aspect of the firm. The DOST evaluates the firm’s positivity and its repayment capability through the abovementioned three aspects. BIDDA aims to determine the significant attributes in the development of data sets of SETUP adopter selection criteria in terms of: a. demographic profile; b. pre-performance business profile; and c. post-performance business profile after S&amp;T intervention. There are also three foundations on the predictive computation for success rate: The Financial which covers the 50%; the Sector which covers the 30%; and the Location which covers the 20% of the total 100% of the success rate. This also helps the MSMEs to determine not only in terms of SETUP program success rate but also in their business success rate. We used Multi-Criteria Decision-Making (MCDM) machine learning model for the predictive analysis in the BIDDA application.

Optimal Consolidation of Polling Locations

Problem definition: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .

Critical Criteria Depicting the Rational Design of Zn Anode Current Collector

AbstractBy virtue of the intrinsic safety and cost‐effectiveness, aqueous Zn‐ion batteries (AZIBs) have gained increasing attention in the realm of energy storage. In spite of the promise of Zn anode, challenges like dendrite growth and side reactions persist as hurdles to be overcome. Addressing this, the rational design of Zn anode current collectors (ZACCs) is an effective solution. Recent years have witnessed the significant strides in the construction of ZACCs, yet the precise pathways for ZACC development remain unclear, lacking a set of specific criteria to guide progress. In this comprehensive review, acknowledging the concerns surrounding Zn anodes, six criteria—electrical conductivity, zincophilicity, orientational deposition inducibility, chemical stability, mechanical durability, and scalability—are innovatively put forward as pivotal benchmarks for the design of ZACCs. Each criterion is accompanied by tailored optimization strategies and corresponding challenges. Furthermore, future trend in ZACCs development is envisaged, along with potential application scenarios for AZIBs. This review will expedite the advancement of ZACCs and contribute to the flourishing landscape of AZIBs.

Scoping Review with Topic Modeling on the Diagnostic Criteria for Degenerative Cervical Myelopathy.

This study is a scoping review. There is a broad variability in the definition of degenerative cervical myelopathy (DCM) and no standardized set of diagnostic criteria to date. We interrogated the Myelopathy.org database, a hand-indexed database of primary clinical studies conducted exclusively on DCM in humans between 2005-2021. The DCM inclusion criteria used in these studies were inputted into 3 topic modeling algorithms: Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), and BERtopic. The emerging topics were subjected to manual labeling and interpretation. Of 1676 reports, 120 papers (7.16%) had well-defined inclusion criteria and were subjected to topic modeling. Four topics emerged from the HDP model: disturbance from extremity weakness and motor signs; fine-motor and sensory disturbance of upper extremity; a combination of imaging and clinical findings is required for the diagnosis; and "reinforcing" (or modifying) factors that can aid in the diagnosis in borderline cases. The LDA model showed the following topics: disturbance to the patient is required for the diagnosis; reinforcing factors can aid in the diagnosis in borderline cases; clinical findings from the extremities; and a combination of imaging and clinical findings is required for the diagnosis. BERTopic identified the following topics: imaging abnormality, typical clinical features, range of objective criteria, and presence of clinical findings. This review provides quantifiable data that only a minority of past studies in DCM provided meaningful inclusion criteria. The items and patterns found here are very useful for the development of diagnostic criteria for DCM.

Informing climate-smart agriculture in low resource settings for practitioners: A review and analysis of interactive tools

Background Agricultural producers in developing countries are uniquely vulnerable to the impacts of climate change and have the least ability to adapt. While there is a growing consensus that more financing and resources are needed to address these impacts, information on how to direct funding and support adaptation is dispersed and difficult to find. Agricultural development stakeholders and investors can leverage increasingly available data from a range of online sources to inform their climate smart agriculture investments, but it is not always clear which data tools are easily accessible and which can support different aspects of their programs. Methods This analysis aims to inform stakeholders how different tools can inform their climate smart investments. Hundreds of interactive tools were reviewed from multiple sources and a set of criteria was developed to simplify and elucidate the landscape of resources available that support adaptation and GHG mitigation for agricultural producers in low-income countries. The search strategy included a literature review, discussions with key stakeholders, and a review of existing databases of tools (e.g., NDC Partnership Toolbox). Results Ultimately 29 tools were identified and compared in terms of how they address both climate risk, adaptation, and mitigation. The data sources behind the tools were also compared, and illustrative user groups were identified. Many valuable, easy-to-use tools exist offering non-climate experts’ opportunities to gain insights into the relationship between climate and small-scale farming systems. However, the tools available are insufficient and should not be relied upon exclusively for informing investments. Conclusions This review provides a valuable resource for those looking to inform investments and programming in small-scale agriculture. This set of tools can provide insights that can be leveraged in various ways for a wide range of users, but they also have considerable limitations. This review can help users understand how these tools can be useful and the types of additional context-specific and local information that should be sought.

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors

Recent studies have shown the potential of wearable sensors for objective detection of health and safety risks in construction workers through their collected physiological data. Body temperature, as the focus of the current study, is one of the most important physiological parameters that can help to detect various health and safety risks such as heat stress, physical fatigue, and infectious diseases. This study aims to assess the applicability and performance of off-the-shelf wearable sensor devices to monitor workers’ body temperature in construction sites by evaluating the accuracy of temperature measurements as well as the comfort of the devices.A total of nine off-the-shelf wearable sensor devices available on the market were initially trialed in the laboratory, and three devices were shortlisted considering a set of selection criteria for further assessment. Over three weeks, the shortlisted wearable sensors were tested on 26 workers in two large construction sites in Australia. The reliability/validity of the selected wearable sensors in measuring body temperature was investigated using Bland-Altman analysis. Human factors were also investigated in terms of the comfort of the devices, their impact on workers’ performance, and the acceptability of being worn for an extended period (i.e., 8 h or more). It was found that all selected devices measured body temperature with a bias of less than one indicating a slight difference in measurements compared to the reference hospital-grade thermometers. Two devices out of the three were also comfortable. The achieved results indicate that it is feasible to develop a continuous temperature monitoring platform using off-the-shelf wearable sensors to detect a range of significant health and safety risks in construction sites objectively. Considering the rapid advancements in manufacturing wearable sensors, future research can adopt a similar approach to include the newly introduced off-the-shelf temperature sensors and select the most appropriate device.

Standardising bee sampling: A systematic review of pan trapping and associated floral surveys.

The use of coloured pan traps (bee bowls, Moericke traps) for sampling bees (and other pollinators) has continuously increased over the last two decades. Although a number of methodological studies and conceptual frameworks offer guidance on standardised sampling, pan trap setups vary widely in characteristics even when optimised for capturing bees. Moreover, some uncertainty persists as to how local flower abundance and diversity influence sampling. We systematically reviewed peer-reviewed studies that used pan traps for bee collection and that were listed in the Web of Science core collection. To gauge methodological variation, we identified a set of relevant methodological criteria and assessed the studies accordingly. For obtaining evidence that pan trap samples and floral environment around traps are correlated, we screened the relevant studies for such correlations. While some aspects of pan trapping (e.g., trap coloration and elevation) were similar in the majority of studies, other aspects varied considerably (e.g., trap volume/diameter and sampling duration). Few studies used floral abundance and/or diversity as an explanatory variable in their analyses of bee samples. Among these studies, we found a considerable variation in key aspects of floral survey methods, such as time and space between vegetation surveys and pan trap sampling, abundance measures (quantitative, semi-quantitative and presence-absence), and processing of raw data prior to analysis. Often studies did not find any correlation between the floral environment and bee samples. Reported correlations varied markedly across studies, even within groups of studies applying a similar method or analysing a similar group of bees. Our synthesis helps to identify key issues of further standardisation of pan trap methodology and of associated floral surveys. In addition to the few aspects that have been standardised over the past decades, we suggest methodological direction for future research using pan traps as a better standardised method for the collection of wild bees. We encourage further studies to illuminate if and how varying floral resources around traps bias bee samples from pan traps. More generally, our synthesis shows that trapping methodologies should be reviewed regularly when their use increases to ensure standardisation.

Protecting Future Generations Through Minilateralism: Climate Clubs and Normative Legitimacy

Despite three decades of global climate negotiations and high expectations for the 2015 Paris Agreement, global emissions continue to grow. To protect future generations from severe harm, scholars, environmentalists, and politicians alike explore potential supplements to the United Nations Framework Convention on Climate Change process. One potential supplement is climate clubs of a type where a small number of “enthusiastic” countries embark on ambitious mitigation efforts while encouraging other, more “reluctant” countries to join. Previous research has shown that this club type possesses a significant potential for expanding membership and eventually becoming highly effective in reducing global emissions. A common criticism of climate clubs, however, is that they lack legitimacy. Assessing this criticism, we argue that climate clubs of the type considered here can be normatively legitimate. The main challenge for normative legitimacy concerns climate clubs’ use of incentives, particularly negative incentives, to attract members. However, we argue that even negative incentives for participation can be legitimate, assuming they meet a set of relevant legitimacy criteria—including that the club respects human rights, provides a comparative benefit, maintains institutional integrity, implements only proportional incentives, and fulfills a requisite set of epistemic criteria. We also argue that the normative legitimacy of climate clubs’ use of incentives for compliance is less challenging than the normative legitimacy of their use of incentives for participation.